This paper presents two hybrid control topologies; the
topologies
are designed by combining artificial intelligence approaches and sliding-mode
control methodology. The first topology mixes the learning algorithm
for multivariable data analysis (LAMDA) approach with sliding-mode
control. The second offers a Takagi–Sugeno multimodel approach,
internal model, and sliding-mode control. The process under study
is a nonlinear pH neutralization process with high nonlinearities
and time-varying parameters. The pH process is simulated for multiple
reference changes, disturbance rejection, and noise in the transmitter.
Performance indices are used to compare the proposed approaches quantitatively.
The hybrid control topologies enhance the performance and robustness
of the pH process under study.
A device laboratory was designed to create a commanded disturbance to the Furuta inverted pendulum. This pendulum was modified by adding a second inverted pendulum coupled to the main one by means of a semi-rigid spring. The induced motion on the second inverted pendulum causes displacement of the center of mass of the system, producing a kind of perturbation similar to that presented on mobile inverted pendulum transportation units. A linear matrix inequality (LMI) controller is designed from the unperturbed model (based on the main pendulum without the second inverted one) and implemented to our system. Then, experimentally, the behaviour of the whole closed-loop system and the controller performance was analysed. According to the laboratory test, the LMI controller is robust enough in front of perturbation induced on the second pendulum.
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